where, \(\sigma _{i,j}\)  counts the total number of shortest paths between \(i\)  and \(j\) , and \(\sigma _{i,j}\left( v \right)\)  counts how many of these
pass through node \(v\) .
Hence, \(b_v\)  gives an indication for the amount of potential control or influence node \(v\)  has on the information flow between all other nodes in the network.
Computing this measure straightforwardly for each node requires a large number of operations, \(\Theta \left( N^3 \right)\) , leading to a run time that
is impractical for large networks. Using Brande's algorithm [1]} a
complexity of \(\Omega \left(M\times N\right)\)  is possible, which is still
time consuming for the networks with millions of nodes.
